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<table width="100%" summary="page for azpro"><tr><td>azpro</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>
azpro
</h2>

<h3>Description</h3>


<p>Data come from the 1991 Arizona cardiovascular patient files. A subset of the 
fields was selected to model the differential length of stay for patients entering 
the hospital to receive one of two standard cardiovascular procedures: CABG and PTCA. 
CABG is the standard acronym for Coronary Artery Bypass Graft, where the flow of 
blood in a diseased or blocked coronary artery or vein has been grafted to bypass 
the diseased sections. PTCA, or Percutaneous Transluminal Coronary Angioplasty, is 
a method of placing a balloon in a blocked coronary artery to open it to blood flow. 
It is a much less severe method of treatment for those having coronary blockage, with 
a corresponding reduction in risk. 
</p>


<h3>Usage</h3>

<pre>data(azpro)</pre>


<h3>Format</h3>

<p>A data frame with 3589 observations on the following 6 variables.
</p>

<dl>
<dt><code>los</code></dt><dd><p>length of hospital stay</p>
</dd>
<dt><code>procedure</code></dt><dd><p>1=CABG;0=PTCA</p>
</dd>
<dt><code>sex</code></dt><dd><p>1=Male; 0=female</p>
</dd>
<dt><code>admit</code></dt><dd><p>1=Urgent/Emerg; 0=elective (type of admission)</p>
</dd>
<dt><code>age75</code></dt><dd><p>1= Age&gt;75; 0=Age&lt;=75</p>
</dd>
<dt><code>hospital</code></dt><dd><p>encrypted facility code (string)</p>
</dd>
</dl>



<h3>Details</h3>


<p>azpro is saved as a data frame.
Count models use los as response variable. 0 counts are structurally excluded  
</p>


<h3>Source</h3>


<p>1991 Arizona Medpar data, cardiovascular patient files, 
National Health Economics &amp; Research Co.
</p>


<h3>References</h3>


<p>Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press
Hilbe, Joseph M (2009), Logistic Regression Models, Chapman &amp; Hall/CRC
</p>


<h3>Examples</h3>

<pre>
data(azpro)
glmazp &lt;- glm(los ~ procedure + sex + admit, family=poisson, data=azpro)
summary(glmazp)
exp(coef(glmazp))
#glmaznb &lt; -glm.nb(los ~ procedure + sex + admit, data=azpro)
#summary(glmaznb)
#exp(coef(glmaznb))
</pre>


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